Why Audience Data Should Be at the Center of Customer Experience
We all interact with a slew of ads each and every day. In some cases, when they offer you something that is not of interest to you, you probably simply ignore them. For example, if the ad mentions the benefits a product or service offers to parents, and you are not a parent, it is probably fairly easy to decide the message is not for you rather than continue reading to learn more about the benefits of a product you clearly do not need. In a world where we see ads at every turn, it is crucial to use audience data to tailor your messages to your customers; otherwise, you will lose their attention within mere seconds.
More than likely, you already collect quite a bit of audience data, whether it is first-, second-, or third-party data. Advertisers often use this audience data to determine how they budget their ad spend to maximize their campaigns’ overall performances. This is something that has been happening for quite a while.
In a nutshell, brands determine different types of audiences they wish to communicate with. These types can be as simple as “frequent purchasers” and “periodic purchasers,” or they can be much more niche like “loyalty members,” “extreme-sports enthusiasts,” or “parents concerned with safety.” No matter how a brand divides its audience, the idea behind using audience data to drive ad spend is to help you understand which campaigns work best for which audiences. This helps you to maximize the limited advertising budget you have.
Personalized messaging and advertising already exist. For instance, if you look at a particular product but do not buy it, it is very likely that product will begin to be advertised to you on other sites you visit. This is called product-level retargeting. What gets really interesting is when you begin to layer audience data on top of personal activity to make personalized messaging more strategic.
For instance, if you can see that your frequent purchasers are all interested in a certain type of product — the data shows that frequent purchasers who purchase a particular product begin to buy it repeatedly — you might begin to personalize messaging to all customers who match that audience criteria. In fact, these campaigns can all look and feel completely different based on which audience segment you are speaking to. Perhaps campaigns to new customers include a welcome message, while campaigns to frequent purchasers remind them how much you value them. The ability to completely customize and personalize this messaging allows you to optimize your customer experience and is only possible if you truly understand your audience data.
Your ads can also move beyond what the product is (its features) to talk about why it should be of interest to your customers (benefits). This allows you to build a frame of reference based on what you know about the audience segment to whom you are speaking. One segment may appreciate the technical nuances of a product while another segment loves hearing how luxurious the same product is.
Building audiences can be an interesting task on its own; it is interesting to see which types of audiences are most likely to be drawn to your brand. But data for data’s sake does not do that much for your brand. Audience data must be acted upon with personalized messaging for it to be actionable and have a real impact on your brand’s bottom line.
The more detailed you are with your audience data, the more precise your ad delivery can be. The level of granularity that you achieve will dictate the flexibility you have to personalize your ads to help customers find the products that are most likely to serve their needs. At the end of the day, happy customers — regardless of which audience segment they are in — are most likely to be return customers.
This post taken from http://blogs.adobe.com
Kalyan Banga200 Posts
I am Kalyan Banga, a Post Graduate in Business Analytics from Indian Institute of Management (IIM) Calcutta, a premier management institute, ranked best B-School in Asia in FT Masters management global rankings. I have spent 6 years in field of Analytics.